Instructions to use ProbeX/Model-J__ResNet__model_idx_0146 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ProbeX/Model-J__ResNet__model_idx_0146 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ProbeX/Model-J__ResNet__model_idx_0146") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0146") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0146", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- dd932f7203bc07bed7cb6857a802c49958463f05b9a6586ddaad468d7a05676f
- Size of remote file:
- 5.37 kB
- SHA256:
- 9ff733d1cd02c3a2d82edcf87debcf388f13636ba3bb2b73399543b8d73caf25
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.